SODet-YOLO: A Small Object Detection Algorithm for UAV Aerial Photography Perspective

To tackle the frequent missed and false detection issues arising from the tiny scale of objects and strong background clutter in UAV aerial photography scenarios, this paper proposes a novel algorithm named SODet-YOLO for UAV aerial imagery. First, to effectively extract the features of aerial objects and alleviate background interference, we integrate a high-resolution detection head denoted as P2 into the YOLO11n, which is connected to the feature layer from the second downsampling stage of the Backbone and Neck networks, we design the Fine-Grained Aggregation-Asymptotic Feature Pyramid Network (FGA-AFPN) to realize adequate fusion of feature information at different levels. Second, we redesign the original C3k2 module by embedding the Inception Depthwise Convolution (IDC). This design effectively expands the receptive field, enriches multi-scale contextual feature extraction, and mitigates adverse interference from complex background clutter. In addition, a novel IoU loss function named MPDInterpIoU is proposed by combining InterpIoU with MPDIoU. This function promotes faster convergence at the early learning stage and optimizes detection-related performance. Finally, the Parallelized Patch-aware Attention (PPA) is incorporated before the downsampling module to preserve the key features of small objects throughout multiple downsampling steps. The experimental findings validate that SODet-YOLO achieves an mAP@0.5 score of 41.487% on the VisDrone2019 object detection dataset, representing an 8.92% performance enhancement relative to the baseline YOLO11n model. However, the computational cost increases moderately, with the number of parameters increasing by 1.08 M, the computational complexity increasing by 26.1 GFLOPs, and the average inference time growing by 34.7 ms.